Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project

Abstract

Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naïve Bayes, Logistic Regression, Logistic Model Tree and Random Forests for predicting incident diabetes using medical records of cardiorespiratory fitness. In addition, we apply different techniques to uncover potential predictors of diabetes. This FIT project study used data of 32,555 patients who are free of any known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 5-year follow-up. At the completion of the fifth year, 5,099 of those patients have developed diabetes. The dataset contained 62 attributes classified into four categories: demographic characteristics, disease history, medication use history, and stress test vital signs. We developed an Ensembling-based predictive model using 13 attributes that were selected based on their clinical importance, Multiple Linear Regression, and Information Gain Ranking methods. The negative effect of the imbalance class of the constructed model was handled by Synthetic Minority Oversampling Technique (SMOTE). The overall performance of the predictive model classifier was improved by the Ensemble machine learning approach using the Vote method with three Decision Trees (Naïve Bayes Tree, Random Forest, and Logistic Model Tree) and achieved high accuracy of prediction (AUC = 0.92). The study shows the potential of ensembling and SMOTE approaches for predicting incident diabetes using cardiorespiratory fitness data.

Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review

“…MLAs have been shown to improve precision in identifying individuals at risk of disease. (5)(6)(7)(8)(9)(10)…”

Multi-sensor fusion based on multiple classifier systems for human activity identification

“…Therefore, balancing the training data would improve the classification algorithm performances. Two methods commonly used for solving the class imbalanced problem are the Random under-sampling (RUS) and Synthetic Minority Oversampling Techniques (SMOTE) [71]. Random undersampling methods reduce the majority classes to equal the number of minority classes.…”

“…Here, we utilize the SMOTE [25] to increase the minority activity classes following a recent study in human activity recognition [6]. We oversampled the minority classes such as Descending stairs, Jumping and descending stairs to solve the problem related to imbalanced dataset [71]. Figure 2 shows the activity class imbalanced distribution in Dataset 1.…”

“…Moreover, AUC is independent of data skewness and class activity distributions. In addition, area under the curve has been used to assess the impact of class distribution in human activity identification and related applications [6,71].…”

Nationwide prediction of type 2 diabetes comorbidities

“…Machine learning (ML) techniques are increasingly being used to analyze electronic health record data to predict future disease onset or its future course [9][10][11][12][13] . These efforts include prediction of onset and complications of cardiovascular disease [14][15][16][17][18][19][20][21] , onset of T2D [22][23][24][25][26] , onset of kidney disease 27 , as well as prediction of postoperative outcomes [28][29][30][31][32] , birth related outcomes 33,34 , mortality 15,35,36 and hospital readmissions [37][38][39][40][41][42][43] . However, current approaches typically suffer from a number of limitations.…”